Unveiling Conversion Reaction on Intercalation‐Based Transition Metal Oxides for High Power, High Energy Aqueous Lithium Battery
Bibliographic record
Abstract
Abstract Aqueous lithium batteries are reaching their energy and power limits partly due to limited capacity from intercalation chemistry. Alternatively, conversion reactions bring added capacity that can significantly increase the capacity ceiling of the cell. However, such reactions can only be realized in organic electrolytes, and similar redox chemistry in aqueous lithium batteries is not reported yet. In this work, it is discovered that a large work function difference (up to 1.78 eV) between crystalline LiMn2O4 (cLMO) and semicrystalline MnO2 (scMO) makes energy bands of MnO2 bend downward toward their interface, resulting in the accumulation of free electrons on scMO and inducing a reversible conversion reaction between scMO and Li+ in aqueous media. Based on this mechanism, a rechargeable hybrid aqueous battery employing hierarchical porous cLMO–scMO composite as a cathode delivers an extremely high cell‐level energy (171 Wh kg−1) and power density (5118 W kg−1) with excellent cycling stability (up to 3000 cycles) in a wide working temperature range (25 to 60 °C). More significantly, similar conversion reactions can also be achieved on LiCoO2, which provides a general design principle to obtain unprecedented energy and power density in commonly used transition metal oxides.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".